Apache Kafka Streams + Machine Learning (Spark, TensorFlow, H2O.ai)

Posted in Analytics, Apache Kafka, Apache Spark, Big Data, Confluent, Hadoop, Integration, Kafka Connect, Kafka Streams, Machine Learning, Messaging, Microservices, Open Source, Stream Processing on May 23rd, 2017 by Kai Wähner

I started at Confluent in May 2017 to work as Technology Evangelist focusing on topics around the open source framework Apache Kafka. I think Machine Learning is one of the hottest buzzwords these days as it can add huge business value in any industry. Therefore, you will see various other posts from me around Apache Kafka (messaging), Kafka Connect (integration), Kafka Streams (stream processing), Confluent’s additional open source add-ons on top of Kafka (Schema Registry, Replicator, Auto Balancer, etc.). I will explain how to leverage all this for machine learning and other big data technologies in real world production scenarios.

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Agile Cloud-to-Cloud Integration with iPaaS, API Management and Blockchain

Posted in API Management, Blockchain, Cloud, Cloud-Native, Docker, EAI, ESB, Microservices, Middleware on April 23rd, 2017 by Kai Wähner

Cloud-to-Cloud integration is part of a hybrid integration architecture. It enables to implement quick and agile integration scenarios without the burden of setting up complex VM- or container-based infrastructures. One key use case for cloud-to-cloud integration is innovation using a fail-fast methodology where you realize new ideas quickly. You typically think in days or weeks, not in months. If an idea fails, you throw it away and start another new idea. If the idea works well, you scale it out and bring it into production to a on premise, cloud or hybrid infrastructure. Finally, you make expose the idea and make it easily available to any interested service consumer in your enterprise, partners or public end users.

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Cloud Native Middleware Microservices – 10 Lessons Learned (O’Reilly Software Architecture 2017, New York)

Posted in API Management, Cloud, Cloud-Native, Docker, EAI, ESB, Microservices, Middleware, SOA on April 5th, 2017 by Kai Wähner

I want to share my slide deck and video recordings from the talk “10 Lessons Learned from Building Cloud Native Middleware Microservices” at O’Reilly Software Architecture April 2017 in New York, USA in April 2017.

Abstract
Microservices are the next step after SOA: Services implement a limited set of functions; services are developed, deployed, and scaled independently; continuous delivery automates deployments. This way you get shorter time to results and increased flexibility. Containers improve things even more, offering a very lightweight and flexible deployment option.

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Case Study: From a Monolith to Cloud, Containers, Microservices

Posted in API Management, Cloud, Cloud-Native, Docker, EAI, ESB, Java / JEE, Microservices, Middleware, SOA on February 24th, 2017 by Kai Wähner

The following shows a case study about successfully moving from a very complex monolith system to a cloud-native architecture. The architecture leverages containers and Microservices. This solve issues such as high efforts for extending the system, and a very slow deployment process. The old system included a few huge Java applications and a complex integration middleware deployment.

The new architecture allows flexible development, deployment and operations of business and integration services. Besides, it is vendor-agnostic so that you can leverage on-premise hardware, different public cloud infrastructures, and cloud-native PaaS platforms.

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Streaming Analytics Comparison of Open Source Frameworks, Products, Cloud Services

Posted in Analytics, Big Data, Business Intelligence, Cloud, Hadoop on November 15th, 2016 by Kai Wähner

In November 2016, I am at Big Data Spain in Madrid for the first time. A great conference with many awesome speakers and sessions about very hot topics such as Apache Hadoop, Spark Spark, Streaming Processing / Streaming Analytics and Machine Learning. If you are interested in big data, then this conference is for you! My two talks:

  • How to Apply Machine Learning to Real Time Processing” (see slides and video recording from a similar conference talk).
  • Comparison of Streaming Analytics Options” (the reason for this blog post; an updated version of my talk from JavaOne 2015)
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Comparison of Open Source IoT Integration Frameworks

Posted in API Management, Cloud, Cloud-Native, Microservices, SOA on November 3rd, 2016 by Kai Wähner

In November 2016, I attended Devoxx conference in Casablanca. Around 1500 developers participated. A great event with many awesome speakers and sessions. Hot topics this year besides Java: Open Source Frameworks, Microservices (of course!), Internet of Things (including IoT Integration), Blockchain, Serverless Architectures.

I had three talks:

  • How to Apply Machine Learning to Real Time Processing
  • Comparison of Open Source IoT Integration Frameworks
  • Tools in Action – Live Demo of Open Source Project Flogo

In addition, I was interviewed by the Voxxed team about Big Data, Machine Learning and Internet of Things. The video will be posted on Voxxed website in the next weeks.

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Open Source Project Flogo – Overview, Architecture and Live Demo

Posted in API Management, Cloud, Cloud-Native, Docker, ESB, Microservices, Middleware, SOA on November 3rd, 2016 by Kai Wähner

In October 2016, the open source IoT integration framework Flogo was published as first developer preview. This blog post is intended to give a first overview about Flogo. You can either browse through the slide deck or watch the videos.

Flogo

What is Project Flogo?

In short, Flogo is an ultra-lightweight integration framework powered by Go programming language. It is open source under the permissive BSD license and easily extendable for your own use cases. Flogo is used to develop IoT edge apps or cloud-native / serverless microservices. Therefore, it is complementary to other integration solutions and IoT cloud platforms.

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Comparison Of Log Analytics for Distributed Microservices – Open Source Frameworks, SaaS and Enterprise Products

Posted in Analytics, Big Data, Business Intelligence, Cloud, Hadoop, Microservices, SOA on October 20th, 2016 by Kai Wähner

I had two sessions at O’Reilly Software Architecture Conference in London in October 2016. It is the first #OReillySACon in London. A very good organized conference with plenty of great speakers and sessions. I can really recommend this conference and its siblings in other cities such as San Francisco or New York if you want to learn about good software architectures and new concepts, best practices and technologies. Some of the hot topics this year besides microservices are DevOps, serverless architectures and big data analytics.

I want to share the slide of my session about comparing open source frameworks, SaaS and Enterprise products regarding log analytics for distributed microservices:

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Trends at JavaOne 2016: Microservices, Docker, Cloud-Native Middleware

Posted in Cloud, Cloud-Native, Docker, ESB, Java / JEE, Microservices, Middleware, SOA on September 23rd, 2016 by Kai Wähner

Like every year, I attended JavaOne (part of Oracle World) in San Francisco in late September 2016. This is still one of the biggest conferences around the world for technical experts like developers and architects.

I planned to write a blog posts about new trends from the program, exhibition and chats with other attendees. Though, I can make it short: Besides focus on Java platform updates (Java 9, Java EE 8, etc.), I saw three hot topics which are highly related to each other: Microservices, Docker and Cloud. It felt like 80% of non-Java talks were about these three topics. The other 20% were Internet of Things (IoT), DevOps and some other stuff. Middleware was also a hot topic. Not always directly, but I was in several talks focusing on integration, orchestration of microservices, (IoT) gateways.

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Streaming Analytics with Analytic Models (R, Spark MLlib, H20, PMML)

Posted in Analytics, Big Data, Business Intelligence, Hadoop, In Memory, NoSQL on March 3rd, 2016 by Kai Wähner

In March 2016, I had a talk at Voxxed Zurich about “How to Apply Machine Learning and Big Data Analytics to Real Time Processing”.

Kai_Waehner_at_Voxxed_Zurich

Finding Insights with R, H20, Apache Spark MLlib, PMML and TIBCO Spotfire

Big Data” is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud.

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